Fast Track Business Outcomes from Artificial Intelligence with Proven Methods and Accelerators

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Deep learning, chat bots, smart machines, natural language processing, neural networks — these buzzwords continue to create much excitement, hype and urgency among business leaders, as they deliberate on how and when to invest in artificial intelligence (AI) to transform the way their companies can leverage data to improve business outcomes and gain competitive advantage.

Working with innovative, early adopters of enterprise AI, Teradata has helped clients realize significant and high-impact business outcomes, such as fraud detection, manufacturing performance optimization, risk modeling, precise recommendation engines and more.

Teradata’s clients are demonstrating that deep learning algorithms are significantly outperforming most rules-based and machine learning approaches, and, in other cases, deep learning is solving previously intractable problems. For example, working with Teradata:

Danske Bank saw a 50 percent increase in fraud detection while also achieving a 60 percent reduction in false positive rates, translating to financial outcomes that are highly significant and material to the business.

A mobile services provider is using deep learning and natural language processing techniques to apply 300-plus routine response types to manage customers’ common questions in two languages, automating routine queries at a much lower cost, so human agents can focus on complex requests and provide more personal customer attention.

A major shipping/logistics distributor now uses AI for image matching techniques that reduce costly lost package resolution time, saving the business $25 million a year.

A government postal service organization now uses AI-driven image recognition and deep learning processes to improve the sorting of over 115 million parcels a year, resulting in valuable operational efficiencies that reduce time and radically lower cost.

However, the reality for most large enterprises is that building AI solutions on their own that address their unique opportunities is not feasible. Challenges abound, from needing to establish an AI strategy that cohesively brings together processes and culture, to solving technical challenges, such as working with a variety of open-source projects, specialized hardware and fragmented data sets to operationalizing and supporting autonomous decisions. Furthermore, the shortage of AI talent simply makes it too difficult for one organization to pursue AI at an affordable, sustainable rate. Large enterprises need a partner who has done it before.

Working with innovative, early adopters of enterprise AI, Teradata has helped clients realize significant and high-impact business outcomes, such as fraud detection, performance optimization, risk modeling, precise recommendation engines and more.

To help ensure successful AI engagements that enable the delivery of faster business results and lower implementation risk, Teradata introduces new services, including:

AI Strategy and Enablement Services — Teradata will review key enterprise capabilities and provide recommendations and next steps to successfully realize business value from AI.

AI Rapid Analytic Consulting Engagement — A service offering designed to help clients quickly demonstrate proof of value to gain buy-in from stakeholders.

AI Platform Build Services — A collaborative client engagement to build and deploy a deep learning platform while also integrating data sources, models and business processes. This includes implementing use cases through data science and engineering.

AI-as-a-Service — Teradata helps clients design and oversee mechanisms to optimize and improve existing business processes using AI. Teradata manages an iterative, stage-gate process for analytic models from development to hand over to operations.

Teradata is also introducing AI “accelerators” composed of best practices, code, IP and proven design patterns.

AnalyticOps Accelerator — Available now, this accelerator provides an end-to-end framework to facilitate the generation, validation, deployment and management of deep learning models at scale.

Financial Crimes Accelerator — Uses deep learning to detect patterns across retail banking products and channels such as credit card, debit card, online, branch banking, ATM, wire transfer and call centers. It continuously monitors and thwarts fraudulent schemes used by criminal actors to exploit the system, leading to quick time to value.

Upcoming AI events

Explore AI in more detail by virtually accessing or attending our informative sessions and panels at the Teradata PARTNERS Conference, from Oct. 22-26. Experts and practitioners will share specific use case scenarios, AI challenges and best practices, and how to identify or develop skills necessary to usher AI into your enterprise.

Also, join the O’Reilly webcast, “Bringing Artificial Intelligence to the Enterprise,” on Oct. 31, 2017, at 10:00 a.m. PST for a discussion of the current state of AI within large enterprises and a look at the trends that will shape their future architectures. You will learn:

How AI is currently driving business outcomes in a variety of industries.

The different options and considerations for enterprises to consume AI technology.

What engineering problems must be solved for AI to go mainstream within large enterprises.

Chad Meley is Vice President of Product Marketing at Teradata, responsible for Teradata’s Analytic Ecosystem, IoT and Artificial Intelligence solutions.

Chad understands trends in the analytics and big data space and leads a team of technology specialists who interpret the needs and expectations of customers while also working with Teradata Labs engineers, consulting teams and technology partners.

Prior to joining Teradata, he led Electronic Arts’ Data Platform organization that supported game development, finance and marketing. Chad has held a variety of other leadership roles centered around data and analytics while at Dell and FedEx.

Chad holds a B.A. in economics from the University of Texas, an MBA from Texas Tech University and performed postgraduate work at the University of Texas.

His professional awards include Best Practice Award for Driving Business Results in Data Warehousing from The Data Warehouse Institute, Marketing Excellence Award from the Direct Marketing Association and Marketing Gold Award from Marketing Sherpa. He is a regular speaker at conferences, including Strata, DataWorks and Teradata Partners.